Security Threat Detection Performance Analysis of a Distributed Architecture WSN

[EN] IoT technologies are becoming more and more common in our daily activities because the networks they create are capable of collecting information, monitoring and controlling remotely. However, these devices are not exempt from security attacks, as they become vulnerable entry points to data net...

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Authors: Arreaga-Alvarado, Nestor Xavier, Estrada, Rebeca, Noboa, Andrés, Vera, Nelson, Blanc Clavero, Sara|||0000-0001-6439-2902
Format: article
Publication Date:2024
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/219894
Online Access:https://riunet.upv.es/handle/10251/219894
Access Level:Open access
Keyword:IoT
Distributed WSN
IDS
NodeMCU
Machine Learning
ANNK-means
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
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oai_identifier_str oai:riunet.upv.es:10251/219894
network_acronym_str ES
network_name_str España
repository_id_str
spelling Security Threat Detection Performance Analysis of a Distributed Architecture WSNArreaga-Alvarado, Nestor XavierEstrada, RebecaNoboa, AndrésVera, NelsonBlanc Clavero, Sara|||0000-0001-6439-2902IoTDistributed WSNIDSNodeMCUMachine LearningANNK-means09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] IoT technologies are becoming more and more common in our daily activities because the networks they create are capable of collecting information, monitoring and controlling remotely. However, these devices are not exempt from security attacks, as they become vulnerable entry points to data networks. The use of traditional methods to secure networks (e.g., Next Generation Firewalls (NGFW), encryption, etc.) is not recommended because the devices used in this type of network are limited in terms of computing power and storage availability (e.g., nodeMCU). In this paper, we propose to design two intrusion detection systems in embedded systems using machine learning (ML) algorithms, Artificial Neural Networks and K-means. In a distributed architecture Wireless Sensor Network scenario (WSN), we evaluate their performance in terms of connection and response times, detection accuracy and intruder detection time. Simulation results show that both models are able to find irregularities in network traffic within milliseconds.ElsevierGrupo de Redes de ComputadoresEscuela Técnica Superior de Ingeniería InformáticaInstituto Universitario de Tecnologías de la Información y ComunicacionesDepartamento de Informática de Sistemas y ComputadoresEscuela Superior Politécnica del LitoralRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/219894reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengEscuela Superior Politécnica del Litoral Escuela Superior Politécnica del Litoral CTI-02-2021 ANÁLISIS DE VULNERABILIDADES DE SEGURIDAD PARA PROTOCOLOS, APLICACIONES Y DIPOSITIVOS EN AMBIENTES IOT.open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2198942026-06-13T07:49:27Z
dc.title.none.fl_str_mv Security Threat Detection Performance Analysis of a Distributed Architecture WSN
title Security Threat Detection Performance Analysis of a Distributed Architecture WSN
spellingShingle Security Threat Detection Performance Analysis of a Distributed Architecture WSN
Arreaga-Alvarado, Nestor Xavier
IoT
Distributed WSN
IDS
NodeMCU
Machine Learning
ANNK-means
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short Security Threat Detection Performance Analysis of a Distributed Architecture WSN
title_full Security Threat Detection Performance Analysis of a Distributed Architecture WSN
title_fullStr Security Threat Detection Performance Analysis of a Distributed Architecture WSN
title_full_unstemmed Security Threat Detection Performance Analysis of a Distributed Architecture WSN
title_sort Security Threat Detection Performance Analysis of a Distributed Architecture WSN
dc.creator.none.fl_str_mv Arreaga-Alvarado, Nestor Xavier
Estrada, Rebeca
Noboa, Andrés
Vera, Nelson
Blanc Clavero, Sara|||0000-0001-6439-2902
author Arreaga-Alvarado, Nestor Xavier
author_facet Arreaga-Alvarado, Nestor Xavier
Estrada, Rebeca
Noboa, Andrés
Vera, Nelson
Blanc Clavero, Sara|||0000-0001-6439-2902
author_role author
author2 Estrada, Rebeca
Noboa, Andrés
Vera, Nelson
Blanc Clavero, Sara|||0000-0001-6439-2902
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Grupo de Redes de Computadores
Escuela Técnica Superior de Ingeniería Informática
Instituto Universitario de Tecnologías de la Información y Comunicaciones
Departamento de Informática de Sistemas y Computadores
Escuela Superior Politécnica del Litoral
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv IoT
Distributed WSN
IDS
NodeMCU
Machine Learning
ANNK-means
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic IoT
Distributed WSN
IDS
NodeMCU
Machine Learning
ANNK-means
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] IoT technologies are becoming more and more common in our daily activities because the networks they create are capable of collecting information, monitoring and controlling remotely. However, these devices are not exempt from security attacks, as they become vulnerable entry points to data networks. The use of traditional methods to secure networks (e.g., Next Generation Firewalls (NGFW), encryption, etc.) is not recommended because the devices used in this type of network are limited in terms of computing power and storage availability (e.g., nodeMCU). In this paper, we propose to design two intrusion detection systems in embedded systems using machine learning (ML) algorithms, Artificial Neural Networks and K-means. In a distributed architecture Wireless Sensor Network scenario (WSN), we evaluate their performance in terms of connection and response times, detection accuracy and intruder detection time. Simulation results show that both models are able to find irregularities in network traffic within milliseconds.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/219894
url https://riunet.upv.es/handle/10251/219894
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Escuela Superior Politécnica del Litoral Escuela Superior Politécnica del Litoral CTI-02-2021 ANÁLISIS DE VULNERABILIDADES DE SEGURIDAD PARA PROTOCOLOS, APLICACIONES Y DIPOSITIVOS EN AMBIENTES IOT.
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422111275089920
score 15,812429

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